Artificial Bee Colony Algorithm Based on Multiple Information Guidance

ZHOU Xin-yu; LIU Ying; WU Yan-lin; GUO Jing-lei

doi:10.12263/DZXB.20220146

您当前的位置：

首页 >

文章列表页 >

Artificial Bee Colony Algorithm Based on Multiple Information Guidance

PAPERS | 更新时间：2025-12-08

- Artificial Bee Colony Algorithm Based on Multiple Information Guidance
- ACTA ELECTRONICA SINICA Vol. 52, Issue 4, Pages: 1349-1363(2024)
- 作者机构：
  
  1.江西师范大学计算机信息工程学院,江西南昌 330022
  2.长沙理工大学计算机与通信工程学院,湖南长沙 410114
  3.华中师范大学计算机学院,湖北武汉 430079
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61966019);Natural Science Foundation of Jiangxi Province(20192BAB207030);Fundamental Research Funds for the Central Universities(CCNU20TS026)
- DOI：10.12263/DZXB.20220146
  CLC： TP18;
- Received：30 January 2022，
  
  Revised：2022-09-19，
  
  Published：25 April 2024
- 稿件说明：
移动端阅览
周新宇,刘颖,吴艳林,等. 基于多元信息引导的人工蜂群算法[J]. 电子学报,2024,52(04):1349-1363.

ZHOU Xin-yu, LIU Ying, WU Yan-lin, et al. Artificial Bee Colony Algorithm Based on Multiple Information Guidance[J]. Acta Electronica Sinica, 2024, 52(04): 1349-1363.
周新宇,刘颖,吴艳林,等. 基于多元信息引导的人工蜂群算法[J]. 电子学报,2024,52(04):1349-1363. DOI：10.12263/DZXB.20220146

ZHOU Xin-yu, LIU Ying, WU Yan-lin, et al. Artificial Bee Colony Algorithm Based on Multiple Information Guidance[J]. Acta Electronica Sinica, 2024, 52(04): 1349-1363. DOI：10.12263/DZXB.20220146

摘要

利用优秀个体增强解搜索方程的开采能力是改进人工蜂群算法的一种主流思路.然而，现有相关工作往往仅以适应度信息作为评价个体的唯一标准，易导致算法出现早熟收敛等问题.本文提出一种多元信息引导的人工蜂群算法，分别设计了基于适应度、位置以及相似度信息的3种解搜索方程，并在雇佣蜂阶段和观察蜂阶段采用了不同的使用方式.同时，为保存侦察蜂阶段的搜索经验，采用一种微调后的邻域搜索机制用于处理被放弃蜜源.在CEC2013测试集和一个实际优化问题上进行了大量实验验证，与6种衍生算法和5种知名的相关改进人工蜂群算法进行了对比，结果表明本文算法性能竞争优势明显，在结果精度和收敛速度上均有更好表现.

Abstract

As one of the main ideas to improve the artificial bee colony (ABC) algorithm

the superior individuals are used to enhance the exploitative capability of the solution search equation. However

in the related works

the fitness information is often considered as the sole criterion for evaluating the individuals

which may easily cause some problems

e.g.

the premature convergence. In this work

an improved ABC variant is proposed based on multiple information guidance

called ABC-MIG. In ABC-MIG

three different solution search equations are designed by using the fitness

position

and similarity information

respectively

and these new solution search equations are used in different ways for the employed bee phase and onlooker bee phase. Meanwhile

to save the search experience for the scout bee phase

a modified neighborhood search strategy is used to handle the abandoned food sources. To verify the effectiveness of ABC-MIG

extensive experiments are carried out on the CEC2013 test suite and one real-world optimization problem

and six derivative algorithms and five well-known improved ABC variants are included in the performance comparison. The results confirm that ABC-MIG has very competitive performance

in terms of the result accuracy and convergence speed.

关键词

Keywords

references

陈健瑞 , 王景璟 , 侯向往 , 等 . 挺进深蓝: 从单体仿生到群体智能 [J ] . 电子学报 , 2021 , 49 ( 12 ): 2458 - 2467 .

CHEN J R , WANG J J , HOU X W , et al . Advance into ocean: From Bionic Monomer to swarm intelligence [J ] . Acta Electronica Sinica , 2021 , 49 ( 12 ): 2458 - 2467 . (in Chinese)

ZHAN Z , SHI L , TAN K C , et al . A survey on evolutionary computation for complex continuous optimization [J ] . Artificial Intelligence Review , 2022 , 55 ( 1 ): 59 - 110 .

ZHOU X Y , WU Y L , ZHONG M S , et al . Artificial bee colony algorithm based on multiple neighborhood topologies [J ] . Applied Soft Computing , 2021 , 111 : 107697 .

ZHU G P , KWONG S . Gbest-guided artificial bee colony algorithm for numerical function optimization [J ] . Applied Mathematics and Computation , 2010 , 217 ( 7 ): 3166 - 3173 .

KONG D , CHANG T , DAI W , et al . An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy [J ] . Information Sciences , 2018 , 442 : 54 - 71 .

GAO W F , SHENG H L , WANG J , et al . Artificial bee colony algorithm based on novel mechanism for fuzzy portfolio selection [J ] . IEEE Transactions on Fuzzy Systems , 2019 , 27 ( 5 ): 966 - 978 .

WANG X , ZHANG Y , SUN X , et al . Multi-objective feature selection based on artificial bee colony: An acceleration approach with variable sample size [J ] . Applied Soft Computing , 2020 , 88 : 106041 .

ZHANG M , PALADE V , WANG Y , et al . Attention-based word embeddings using artificial bee colony algorithm for aspect-level sentiment classification [J ] . Information Sciences , 2021 , 545 : 713 - 738 .

ZHOU X Y , LU J X , HUANG J H , et al . Enhancing artificial bee colony algorithm with multi-elite guidance [J ] . Information Sciences , 2021 , 543 : 242 - 258 .

WANG H , WU Z J , RAHNAMAYAN S , et al . Multi-strategy ensemble artificial bee colony algorithm [J ] . Information Sciences , 2014 , 279 : 587 - 603 .

KIRAN M S , HAKLI H , GUNDUZ M , et al . Artificial bee colony algorithm with variable search strategy for continuous optimization [J ] . Information Sciences , 2015 , 300 : 140 - 157 .

ZHOU X Y , WANG H , WANG M W , et al . Enhancing the modified artificial bee colony algorithm with neighborhood search [J ] . Soft Computing , 2017 , 21 ( 10 ): 2733 - 2743 .

PENG H , DENG C S , WU Z J , et al . Best neighbor-guided artificial bee colony algorithm for continuous optimization problems [J ] . Soft Computing , 2019 , 23 ( 18 ): 8723 - 8740 .

LIANG J J , QU B Y , SUGANTHAN P N , et al . Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization [R ] . Zhengzhou : Zhengzhou University , 2013 .

DERRAC J , GARCÍA S , MOLINA D , et al . A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms [J ] . Swarm and Evolutionary Computation , 2011 , 1 ( 1 ): 3 - 18 .

WANG H , WANG W J , ZHOU X Y , et al . Artificial bee colony algorithm based on knowledge fusion [J ] . Complex & Intelligent Systems , 2021 , 7 ( 3 ): 1139 - 1152 .

XUE Y , JIANG J M , ZHAO B P , et al . A self-adaptive artificial bee colony algorithm based on global best for global optimization [J ] . Soft Computing , 2018 , 22 ( 9 ): 2935 - 2952 .

MLADENOVIĆ N , PETROVIĆ J , KOVAČEVIĆ-VUJČIĆ V , et al . Solving spread spectrum radar polyphase code design problem by tabu search and variable neighborhood search [J ] . European Journal of Operational Research , 2003 , 151 ( 2 ): 389 - 399 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Enhanced Artificial Bee Colony Algorithm to Solve Semiconductor Final Test Scheduling Problem

Hybrid Grey Wolf Optimizer with Artificial Bee Colony and Its Application to Clustering Optimization

Artificial Bee Colony Algorithm with Chaotic Catfish Effect and Its Application

A Review of Encryption Traffic Analysis Methods Based on Large Language Models

“面向人机物融合场景的泛在操作系统与环境研究进展”专栏

Related Author

QIAN Bin

LÜ Yang

HU Rong

ZHANG Zi-qi

HUANG Ling-ling

LIU San-yang

GAO Wei-feng

CHENG Jin-feng

Related Institution

Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology

Faculty of Information Engineering and Automation, Kunming University of Science and Technology

School of scienceChina University of PetroleumQingdaoShandong266580China

Department of Applied MathematicsXidian UniversityXi'anShaanxi 710071China

School of science,China University of Petroleum

⁰